Multi-param LSTM input
So you want your network to represent those 3 values at each step as single composite value? I can't think of any better way than utilizing 3 LSTM units but attaching them to same write and read nodes of the enclosing network. In other words your assumption that it makes sense to keep all those 3 values together, gets hardcoded into your network by making 6 connections (3 connected to read and 3 to write) share their weights. Usually researchers leave it to the network to decide whether to keep such composite values together or separate (by learning read and write weights through backpropagation) and sometimes network chooses to keep them together all on its own like in https://youtu.be/93rzMHtYT_0?t=531 (it can be seen how network resets whole bunch of LSTM units simultaneously ).